Distilling Conceptual Connections from MeSH Co-Occurrences Padmini - - PowerPoint PPT Presentation

Distilling Conceptual Connections from MeSH Co-Occurrences

Padmini Srinivasan, Dimitar Hristovski presented @ MEDINFO 2004

15 February 2005 INLS 279 Bioinformatics Seminar Patrick Herron SILS, UNC Chapel Hill

Goals

• Analyze MeSH heading/subheading pair co-occurrences,

e.g.,

( diabetes / drug therapy ) with ( chemical / therapeutic use )

heading/concept subheading heading/concept subheading

• Interestingness: Select semantically meaningful ones

(via chi-sq) that are relatively domain independent

• Develop a “reasonable representation” of each pair: “a

weighted vector […] [emphasizing] verb based functional aspects of the underlying semantics”

• The ultimate goal: such pairs may aid in generating

connections across disciplines across all of MEDLINE

Reducing the problem space: using the SN

• In order to reduce scale of the problem space…

20,742 concepts * 82 headings=1.7 mil heading/subheading pairs, 1.7mil2/2 (number of possible heading subheading pairs) = 1.5 trillion

• and in order to be as domain-independent as

possible…

• Represent concepts by their semantic types in

the SN

• 20,742 -> 134 (semantic type/subheading)
• Problem space down to

(134*82) 2/2 = 60 mil (two orders of magnitude smaller)

• Only 1 mil of that 60 mil is meaningful

Method

• Usual approaches vs. Srinivasan et al
• Extraction of [(st/sh)i, (st/sh)j] pairs
• Selection of background dataset
• Analysis of a single pair

Usual approaches

• First hand-pick relevant verbs & then

extract their arguments

• Set of 1 mil co-occurrences too big for

manual approach

• We want to extract interesting verbs based
• n MeSH co-occurrence

Srinivasan et al approach

• Two step approach:
• 1. automatically identify/extract key verbs

associated with a [(st/sh)i, (st/sh)j] pair

• 2. Use these verbs to extract highly related Ns

and NPs

• This paper establishes one method for

step 1; work on step 2 is for a later date

• We want a weighted verb vector for MeSH

co-occurrences

Extraction of [(st/sh)i, (st/sh)j] pairs

• Corpus: MEDLINE to 2001 into rows with MEDLINE

record id (MRI), Head/subhead

• Transformed into MRI, Sem Type/subhead
• Then [(st/sh)i, (st/sh)j] pairings picked & frequencies

noted

• 30% 1x, 97% < 500 over 11 million records
• Pairings further culled by two more criteria: 1. freq > 500)

down to 31,000 pairs & 2. (observed co-occurrence >= 1.25*expected co-occurrence) lowered total to 22,000 pairs

• 250 randomly selected; documents were reliably

retrieved for 228 of those

Co-occurrence calculations

• Actual co-occurrence of pair A,B

(# docs w/ A * # docs w/B) / (total number of documents)2

• Expected co-occurrence of pair A,B

(# docs w/ A * # docs w/B) / total number of documents

• (Expected – observed)/expected * 100 > 0.25

Selection of background dataset

• 100,000 MEDLINE records randomly selected
• Title + abstract POS-tagged
• Verbs extracted & used as vector to represent

doc; verbs transformed to infinitives

• IDF for each verb as log2(100,000/df)
• BV: background vector: set of (verb, IDF) pairs

for each record – our doc vector D???

Analysis of a single pair

• Identify all docs in which pair appears
• 2/3rds of docs placed in training set
• Other 1/3rd plus random 1/3rd from

MEDLINE in test set

• Create verb profile for pair
• Test verb profiles

Creating verb profile

• Docs POS-tagged & Vs extracted
• Rules for inclusion: V must occur in at least 5

docs; V frequency in training set must be significantly different from its freq in BV – using Pearson’s test (null hypothesis: difference between expected and observed frequencies is random)

• Formation of the profile vector, to which weights

are assigned just like with the BV, BUT..

Formation of the profile vector

• Four different profile vectors are formed:

– V,AugTF – V,AugTF*IDF – V,TF*IDF – V,IDF

• Empirical question as to how each

performs

Testing profiles

• Test each of the 4 PVs against the doc vectors

D of the random set and that 1/3rd of documents in which pairs occur

• Similarity of a background vector to a PV as dot

product of two vectors (D, PV)

• Mean similarity (D, PV) of random set & mean

similarity of topic set calculated

• Variance/information/interestingness: significant

difference in similarities for a pair indicates interestingness

Results: Verb profiles

Withdraw, warrant, undertake, treat, tolerate [(disease or syndrome, drug) & (lipid, adverse effect)]

Verbs Pair

• Authors claim these verb profiles will provide

useful constraints for extracting pair-associated nouns

• Example of top five

verbs from the AugTFIDF PV

Results: test set

Results: evaluation

• Each bar represents one of the 228 pairs

plus standard error

• If a line touches the diagonal then the

similarity is possibly random

• All pairs show significant similarity
• “in the right direction”

Conclusions

• Verbs are important
• Verb profiles from docs with MeSH co-
• ccurrence pairs are different from docs

not covered by pair: verb profiles can be used to characterize other docs w/ co-

• ccurrenc

Questions

• How did Srinivasan et al decide only 1 mil of the

60 mil possible [(st/sh), (st/sh)] pairings are meaningful? Chi sq test with null hypothesis that pairings are random?

• What is the meaning of those similarity values?

How significant???

• Once we have noun-verb representations of

MeSH co-occurrences, what does that get us?

• What’s truly interesting about MeSH co-
• ccurrence? Connecting otherwise disparate

pieces of the literature